Brigham Young University BYU ScholarsArchive All Theses and Dissertations 2016-06-01 A Semi-Automated Algorithm for Segmenting the Hippocampus in Patient and Control Populations Nathan McKay Muncy Brigham Young University Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Physiology Commons BYU ScholarsArchive Citation Muncy, Nathan McKay, "A Semi-Automated Algorithm for Segmenting the Hippocampus in Patient and Control Populations" (2016). All Theses and Dissertations. 6421. https://scholarsarchive.byu.edu/etd/6421 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. A Semi-Automated Algorithm for Segmenting the Hippocampus in Patient and Control Populations Nathan McKay Muncy A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Neuroscience Christopher B. Kirwan, Chair Michael D. Brown Jonathan J. Wisco Department of Physiology and Developmental Biology Brigham Young University June 2016 Copyright © 2016 Nathan McKay Muncy All Rights Reserved ABSTRACT A Semi-Automated Algorithm for Segmenting the Hippocampus in Control and Patient Populations Nathan McKay Muncy Department of Physiology and Developmental Biology Master of Science Neuroscience Calculating hippocampal volume from Magnetic Resonance (MR) images is an essential task in many studies of neurocognition in healthy and diseased populations. The `gold standard' method involves hand tracing, which is accurate but laborious, requiring expertly trained researchers and significant amounts of time. As such, segmenting large datasets with the standard method is impractical. Current automated pipelines are inaccurate at hippocampal demarcation and volumetry. We developed a semi-automated hippocampal segmentation pipeline based on the Advanced Normalization Tools (ANTs) suite of programs to segment the hippocampus. We applied the semi-automated segmentation pipeline to 70 participant scans (26 female) from groups that included participants diagnosed with autism spectrum disorder, healthy older adults (mean age 74) and healthy younger controls. We found that hippocampal segmentations obtained with the semi-automated pipeline more closely matched the segmentations of an expert rater than those obtained using FreeSurfer or the segmentations of novice raters. Further, we found that the pipeline performed best when including manually- placed landmarks and when using a template generated from a heterogeneous sample (that included the full variability of group assignments) than a template generated from more homogeneous samples (using only individuals within a given age or with a specific neuropsychiatric diagnosis). Additionally, the semi-automated pipeline required much less time (5 minutes per brain) than manual segmentation (30-60 minutes per brain) or FreeSurfer (8 hours per brain). Keywords: hippocampus, segmentation, algorithm, autism, advanced normalization tools (ANTs) ACKNOWLEDGMENTS I’d like to thank Dr. Kirwan for brainstorming this project and letting me work on it. He was so patient in teaching me the basic skills I needed and while we figured out the algorithm, and how to best represent the algorithm in the paper. I’d also like to thank Dr. Hunsaker, whose code I stalked heavily while learning how to use ANTs. Additionally, this paper would have been impossible if it were not for Chris Doxey, who answered all my scripting questions and spent so much time segmenting all the scans by hand. Finally, I’d like to thank Ariana Hedges for helping me to develop the repeated measures scripts needed, the analyses would have been impossible without her. TABLE OF CONTENTS TITLE PAGE .................................................................................................................................. i ABSTRACT .................................................................................................................................... ii ACKNOWLEDGMENTS ............................................................................................................. iii TABLE OF CONTENTS ............................................................................................................... iv LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ...................................................................................................................... vii INTRODUCTION .......................................................................................................................... 1 METHODS ..................................................................................................................................... 6 Participants .................................................................................................................................. 6 MRI Data Acquisition ................................................................................................................. 6 Manual Hippocampal Segmentation ........................................................................................... 7 The Semi-Automated Pipeline .................................................................................................... 7 FreeSurfer ................................................................................................................................... 9 Variants of the SAP .................................................................................................................... 9 RESULTS ..................................................................................................................................... 10 Comparison of SAP to FreeSurfer and Manual Segmentation ................................................. 10 Analysis of Variations on the Semi-Automated Pipeline ......................................................... 11 DISCUSSION ............................................................................................................................... 12 Overview ................................................................................................................................... 12 Researcher and Computational Time Considerations ............................................................... 12 iv SegAdapter ................................................................................................................................ 13 Application to Other Structures ................................................................................................ 14 Limitations ................................................................................................................................ 14 Conclusion ................................................................................................................................ 16 REFERENCES ............................................................................................................................. 24 APPENDIX A: Scripting Commands ........................................................................................... 29 CURRICULUM VITAE ............................................................................................................... 30 v LIST OF TABLES Table 1: Various Segmentation Protocols..................................................................................... 17 Table 2: Repeated Measures ANOVA of the Segmentation Methods ......................................... 18 Table 3: Comparison of Variations of the SAP Method ............................................................... 18 vi LIST OF FIGURES Figure 1: Distribution of Hippocampal Volumes per Segmentation Method ............................... 19 Figure 2: Distribution of DSCs ..................................................................................................... 20 Figure 3: Comparison of Exp, SAP, SAPXL, and SAPH ............................................................. 21 Figure 4: Group-Specific Comparisons ........................................................................................ 22 Figure 5: Comparison of Segmentation Hippocampal Masks ...................................................... 23 vii INTRODUCTION Cortical and subcortical segmentation is a useful morphometric tool used for both research and diagnostic purposes. Segmentation involves the process of labeling and separating voxels associated with regions-of-interest (ROIs) (Fischl et al., 2002; Pruessner et al., 2000; Yassa & Stark, 2009). This process, i.e. pipeline, is expedient for use in studying typical and atypical brain morphologies, as typical morphologic variations occur naturally and manifest when controlling for age and sex (Allen, Bruss, Brown, & Damasio, 2005; Allen, Damasio, & Grabowski, 2002; Avants, Yushkevich, et al., 2010; Avants, Cook, et al., 2010; Bartley, Jones, & Weinberger, 1997; Cox, 1996; Evans, 2006; Persson et al., 2014) and atypical morphologies are associated with trauma and a variety of neurodegenerative, developmental, and psychiatric disorders (Csernansky et al., 1998; Csernansky et al., 2002; Csernansky et al., 2005; Fischl et al., 2002; Sparks et al., 2002; Wang et al., 2006; Yassa